Feature Guidance GAN for High Quality Image Restoration

摘要

Here we propose a novel image inpainting model DFG-GAN, which can effectively alleviate the artifacts problem when the missing region area is too large. Unlike other image inpainting models, our model can transfer the image inpainting task into a GAN task when the mask fills the total image. Apart from that, we also take advantage of the extra class label information to tell what kind of the damaged image is. The more information feed in, the better result shall be. Experiments on several publicly available datasets demonstrate the advantage of the proposed method over existing approaches, regarding both visual fidelity and margin texture.